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Modelling the cost of community interventions to reduce child mortality in South Africa using the Lives Saved Tool (LiST).

ABSTRACT: OBJECTIVE:To estimate the costs and impact on reducing child mortality of scaling up interventions that can be delivered by community health workers at community level from a provider's perspective. SETTING:In this study, we used the Lives Saved Tool (LiST), a module in the spectrum software. Within the spectrum software, LiST interacts with other modules, the AIDS Impact Module, Family Planning Module and Demography Projections Module (Dem Proj), to model the impact of more than 60 interventions that affect cause-specific mortality. PARTICIPANTS:DemProj Based on National South African Data. INTERVENTIONS:A total of nine interventions namely, breastfeeding promotion, complementary feeding, vitamin supplementation, hand washing with soap, hygienic disposal of children's stools, oral rehydration solution, oral antibiotics for the treatment of pneumonia, therapeutic feeding for wasting and treatment for moderate malnutrition. PRIMARY AND SECONDARY OUTCOME MEASURES:Reducing child mortality. RESULTS:A total of 9 interventions can prevent 8891 deaths by 2030. Hand washing with soap (21%) accounts for the highest number of deaths prevented, followed by therapeutic feeding (19%) and oral rehydration therapy (16%). The top 5 interventions account for 77% of all deaths prevented. At scale, an estimated cost of US$169.5?million (US$3 per capita) per year will be required in community health worker costs. CONCLUSION:The use of community health workers offers enormous opportunities for saving lives. These programmes require appropriate financial investments. Findings from this study show what can be achieved if concerted effort is channelled towards the identified set of life-saving interventions.

SUBMITTER: Nkonki LL

PROVIDER: S-EPMC5577872 | biostudies-other | 2017 Aug

REPOSITORIES: biostudies-other

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Modelling the cost of community interventions to reduce child mortality in South Africa using the Lives Saved Tool (LiST).

Nkonki Lungiswa Ll LL Chola Lumbwe L LL Tugendhaft Aviva A AA Hofman Karen K KK

BMJ open 20170828 8

<h4>Objective</h4>To estimate the costs and impact on reducing child mortality of scaling up interventions that can be delivered by community health workers at community level from a provider's perspective.<h4>Setting</h4>In this study, we used the Lives Saved Tool (LiST), a module in the spectrum software. Within the spectrum software, LiST interacts with other modules, the AIDS Impact Module, Family Planning Module and Demography Projections Module (Dem Proj), to model the impact of more than ...[more]

PMID: 28851766

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Project description:BackgroundKwaZulu-Natal province in South Africa has the largest population of children under the age of five and experiences the highest number of child births per annum in the country. Its population has also been ravaged by the dual epidemics of HIV and TB and it has struggled to meet targets for maternal and child mortality. In South Africa's federal system, provinces have decision-making power on the prioritization and allocation of resources within their jurisdiction. As part of strategic planning for 2015-2019, KwaZulu-Natal provincial authorities requested an assessment of current mortality levels in the province and identification and costing of priority interventions for saving additional maternal, newborn and child lives, as well as preventing stillbirths in the province.MethodsThe Lives Saved Tool (LiST) was used to determine the set of interventions, which could save the most additional maternal and child lives and prevent stillbirths from 2015-2019, and the costs of these. The impact of family planning was assessed using two scenarios by increasing baseline coverage of modern contraception by 0.5 percentage points or 1 percentage point per annum.ResultsA total of 7,043 additional child and 297 additional maternal lives could be saved, and 2,000 stillbirths could be prevented over five years. Seventeen interventions account for 75% of additional lives saved. Increasing family planning contributes to a further reduction of up to 137 maternal and 3,168 child deaths. The set of priority interventions scaled up to achievable levels, with no increase in contraception would require an additional US$91 million over five years or US$1.72 per capita population per year. By increasing contraceptive prevalence by one percentage point per year, overall costs to scale up to achievable coverage package, decrease by US$24 million over five years.ConclusionFocused attention on a set of key interventions could have a significant impact on averting stillbirths and maternal and neonatal mortality in KwaZulu-Natal. Concerted effort to prioritize family planning will save more lives overall and has the potential to decrease costs in other areas of maternal and child care.

Project description:Achieving the Sustainable Development Goals will require careful allocation of resources in order to achieve the highest impact. The Lives Saved Tool (LiST) has been used widely to calculate the impact of maternal, neonatal and child health (MNCH) interventions for program planning and multi-country estimation in several Lancet Series commissions. As use of the LiST model increases, many have expressed a desire to cost interventions within the model, in order to support budgeting and prioritization of interventions by countries. A limited LiST costing module was introduced several years ago, but with gaps in cost types. Updates to inputs have now been added to make the module fully functional for a range of uses.This paper builds on previous work that developed an initial version of the LiST costing module to provide costs for MNCH interventions using an ingredients-based costing approach. Here, we update in 2016 the previous econometric estimates from 2013 with newly-available data and also include above-facility level costs such as program management. The updated econometric estimates inform percentages of intervention-level costs for some direct costs and indirect costs. These estimates add to existing values for direct cost requirements for items such as drugs and supplies and required provider time which were already available in LiST Costing.Results generated by the LiST costing module include costs for each intervention, as well as disaggregated costs by intervention including drug and supply costs, labor costs, other recurrent costs, capital costs, and above-service delivery costs. These results can be combined with mortality estimates to support prioritization of interventions by countries.The LiST costing module provides an option for countries to identify resource requirements for scaling up a maternal, neonatal, and child health program, and to examine the financial impact of different resource allocation strategies. It can be a useful tool for countries as they seek to identify the best investments for scarce resources. The purpose of the LiST model is to provide a tool to make resource allocation decisions in a strategic planning process through prioritizing interventions based on resulting impact on maternal and child mortality and morbidity.

Project description:The worldwide burden of stillbirths is large, with an estimated 2.6 million babies stillborn in 2015 including 1.3 million dying during labour. The Every Newborn Action Plan set a stillbirth target of ?12 per 1000 in all countries by 2030. Planning tools will be essential as countries set policy and plan investment to scale up interventions to meet this target. This paper summarises the approach taken for modelling the impact of scaling-up health interventions on stillbirths in the Lives Saved tool (LiST), and potential future refinements.The specific application to stillbirths of the general method for modelling the impact of interventions in LiST is described. The evidence for the effectiveness of potential interventions to reduce stillbirths are reviewed and the assumptions of the affected fraction of stillbirths who could potentially benefit from these interventions are presented. The current assumptions and their effects on stillbirth reduction are described and potential future improvements discussed.High quality evidence are not available for all parameters in the LiST stillbirth model. Cause-specific mortality data is not available for stillbirths, therefore stillbirths are modelled in LiST using an attributable fraction approach by timing of stillbirths (antepartum/ intrapartum). Of 35 potential interventions to reduce stillbirths identified, eight interventions are currently modelled in LiST. These include childbirth care, induction for prolonged pregnancy, multiple micronutrient and balanced energy supplementation, malaria prevention and detection and management of hypertensive disorders of pregnancy, diabetes and syphilis. For three of the interventions, childbirth care, detection and management of hypertensive disorders of pregnancy, and diabetes the estimate of effectiveness is based on expert opinion through a Delphi process. Only for malaria is coverage information available, with coverage estimated using expert opinion for all other interventions. Going forward, potential improvements identified include improving of effectiveness and coverage estimates for included interventions and addition of further interventions.Known effective interventions have the potential to reduce stillbirths and can be modelled using the LiST tool. Data for stillbirths are improving. Going forward the LiST tool should seek, where possible, to incorporate these improving data, and to continually be refined to provide an increasingly reliable tool for policy and programming purposes.

Project description:BackgroundDiarrhea is a leading cause of morbidity and mortality among children under five years of age. The Lives Saved Tool (LiST) is a model used to calculate deaths averted or lives saved by past interventions and for the purposes of program planning when costly and time consuming impact studies are not possible.DiscussionLiST models the relationship between coverage of interventions and outputs, such as stunting, diarrhea incidence and diarrhea mortality. Each intervention directly prevents a proportion of diarrhea deaths such that the effect size of the intervention is multiplied by coverage to calculate lives saved. That is, the maximum effect size could be achieved at 100% coverage, but at 50% coverage only 50% of possible deaths are prevented. Diarrhea mortality is one of the most complex causes of death to be modeled. The complexity is driven by the combination of direct prevention and treatment interventions as well as interventions that operate indirectly via the reduction in risk factors, such as stunting and wasting. Published evidence is used to quantify the effect sizes for each direct and indirect relationship. Several studies have compared measured changes in mortality to LiST estimates of mortality change looking at different sets of interventions in different countries. While comparison work has generally found good agreement between the LiST estimates and measured mortality reduction, where data availability is weak, the model is less likely to produce accurate results. LiST can be used as a component of program evaluation, but should be coupled with more complete information on inputs, processes and outputs, not just outcomes and impact.SummaryLiST is an effective tool for modeling diarrhea mortality and can be a useful alternative to large and expensive mortality impact studies. Predicting the impact of interventions or comparing the impact of more than one intervention without having to wait for the results of large and expensive mortality studies is critical to keep programs focused and results oriented for continued reductions in diarrhea and all-cause mortality among children under five years of age.

Project description:Background:As one of several countries that pledged to achieve the Millennium Development Goals (MDGs), Mozambique sought to reduce child, neonatal, and maternal mortality by two thirds by 2015. This study examines the impact of Mozambique's efforts between 1997 and 2015, highlighting the increases in intervention coverage that contributed to saving the most lives. Methods:A retrospective analysis of available household survey data was conducted using the Lives Saved Tool (LiST). Baseline mortality rates, cause-of-death distributions, and coverage of child, neonatal, and maternal interventions were entered as inputs. Changes in mortality rates, causes of death, and additional lives saved were calculated as results. Due to limited coverage data for the year 2015, we reported most results for the period 1997-2011. For 2011-2015 we reported additional lives saved for a subset of interventions. All analyses were performed at national and provincial level. Results:Our modelled estimates show that increases in intervention coverage from 1997 to 2011 saved an additional 422?282 child lives (0-59 months), 85?450 neonatal lives (0-1 month), and 6528 maternal lives beyond those already being saved at baseline coverage levels in 1997. Malaria remained the leading cause of child mortality from 1997 to 2011; prematurity, asphyxia, and sepsis remained the leading causes of neonatal mortality; and hemorrhage remained the leading cause of maternal mortality. Interventions to reduce acute malnutrition and promote artemisinin-based combination therapy (ACT) for malaria were responsible for the largest number of additional child lives saved in the 1997-2011 period. Increases in coverage of delivery management were responsible for most additional newborn and maternal lives saved in both periods in Mozambique. Conclusion:Mozambique has made impressive gains in reducing child mortality since 1997. Additional effort is needed to further reduce maternal and neonatal mortality in all provinces. More lives can be saved by continuing to increase coverage of existing health interventions and exploring new ways to reach underserved populations.

Project description:Background:A cluster randomised trial (CRT) in Burkina Faso was the first to demonstrate that a radio campaign increased health-seeking behaviours, specifically antenatal care attendance, health facility deliveries and primary care consultations for children under 5 years. Methods:Under-five consultation data by diagnosis was obtained from primary health facilities in trial clusters, from January 2011 to December 2014. Interrupted time-series analyses were conducted to assess the intervention effect by time period on under-five consultations for separate diagnosis categories that were targeted by the media campaign. The Lives Saved Tool was used to estimate the number of under-five lives saved and the per cent reduction in child mortality that might have resulted from increased health service utilisation. Scenarios were generated to estimate the effect of the intervention in the CRT study areas, as well as a national scale-up in Burkina Faso and future scale-up scenarios for national media campaigns in five African countries from 2018 to 2020. Results:Consultations for malaria symptoms increased by 56% in the first year (95%?CI 30% to 88%; p<0.001) of the campaign, 37% in the second year (95%?CI 12% to 69%; p=0.003) and 35% in the third year (95%?CI 9% to 67%; p=0.006) relative to the increase in the control arm. Consultations for lower respiratory infections increased by 39% in the first year of the campaign (95%?CI 22% to 58%; p<0.001), 25% in the second (95%?CI 5% to 49%; p=0.010) and 11% in the third year (95%?CI -20% to 54%; p=0.525). Diarrhoea consultations increased by 73% in the first year (95%?CI 42% to 110%; p<0.001), 60% in the second (95%?CI 12% to 129%; p=0.010) and 107% in the third year (95%?CI 43% to 200%; p<0.001). Consultations for other diagnoses that were not targeted by the radio campaign did not differ between intervention and control arms. The estimated reduction in under-five mortality attributable to the radio intervention was 9.7% in the first year (uncertainty range: 5.1%-15.1%), 5.7% in the second year and 5.5% in the third year. The estimated number of under-five lives saved in the intervention zones during the trial was 2967 (range: 1110-5741). If scaled up nationally, the estimated reduction in under-five mortality would have been similar (9.2% in year 1, 5.6% in year 2 and 5.5% in year 3), equating to 14?888 under-five lives saved (range: 4832-30 432). The estimated number of lives that could be saved by implementing national media campaigns in other low-income settings ranged from 7205 in Burundi to 21?443 in Mozambique. Conclusion:Evidence from a CRT shows that a child health radio campaign increased under-five consultations at primary health centres for malaria, pneumonia and diarrhoea (the leading causes of postneonatal child mortality in Burkina Faso) and resulted in an estimated 7.1% average reduction in under-five mortality per year. These findings suggest important reductions in under-five mortality can be achieved by mass media alone, particularly when conducted at national scale.

Dataset Information

Modelling the cost of community interventions to reduce child mortality in South Africa using the Lives Saved Tool (LiST).

Publications

Modelling the cost of community interventions to reduce child mortality in South Africa using the Lives Saved Tool (LiST).

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